Papers LLM-generated Text Detection
“LLM-generated Text Detection” 태그가 달린 논문 22편 · 필터 해제
PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
With the popularity of large language models (LLMs), undesirable societal problems like misinformation production and academic misconduct have been more severe, making LLM-generated text detection now of unprecedented im…
LLM-generated Text DetectionMisinformationText DetectionShort-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion
The malicious usage of large language models (LLMs) has motivated the detection of LLM-generated texts. Previous work in topological data analysis shows that the persistent homology dimension (PHD) of text embeddings can…
LLM-generated Text DetectionText DetectionTopological Data AnalysisZero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities
Verifying the provenance of content is crucial to the function of many organizations, e.g., educational institutions, social media platforms, firms, etc. This problem is becoming increasingly challenging as text generate…
LLM-generated Text DetectionMisinformationText DetectionText GenerationGlimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection
Advanced large language models (LLMs) can generate text almost indistinguishable from human-written text, highlighting the importance of LLM-generated text detection. However, current zero-shot techniques face challenges…
LLM-generated Text DetectionText DetectionRobust Detection of LLM-Generated Text: A Comparative Analysis
The ability of large language models to generate complex texts allows them to be widely integrated into many aspects of life, and their output can quickly fill all network resources. As the impact of LLMs grows, it becom…
Binary ClassificationLLM-generated Text DetectionText DetectionUnderstanding the Effects of Human-written Paraphrases in LLM-generated Text Detection
Natural Language Generation has been rapidly developing with the advent of large language models (LLMs). While their usage has sparked significant attention from the general public, it is important for readers to be awar…
LLM-generated Text DetectionText DetectionText GenerationDetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
Detecting text generated by large language models (LLMs) is of great recent interest. With zero-shot methods like DetectGPT, detection capabilities have reached impressive levels. However, the reliability of existing det…
BenchmarkingLLM-generated Text DetectionText DetectionGigaCheck: Detecting LLM-generated Content
With the increasing quality and spread of LLM-based assistants, the amount of LLM-generated content is growing rapidly. In many cases and tasks, such texts are already indistinguishable from those written by humans, and …
Binary text classificationBoundary DetectionLLM-generated Text DetectionText Classification+1Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement
The rapid development of large language models (LLMs), like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns about misinformation, data biases, and pri…
Binary ClassificationLLM-generated Text DetectionMisinformationMulti-class Classification+1Training-free LLM-generated Text Detection by Mining Token Probability Sequences
Large language models (LLMs) have demonstrated remarkable capabilities in generating high-quality texts across diverse domains. However, the potential misuse of LLMs has raised significant concerns, underscoring the urge…
LLM-generated Text DetectionText DetectionTime Series AnalysisLearning to Rewrite: Generalized LLM-Generated Text Detection
Large language models (LLMs) can be abused at scale to create non-factual content and spread disinformation. Detecting LLM-generated content is essential to mitigate these risks, but current classifiers often fail to gen…
LLM-generated Text DetectionText DetectionDetecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated
As LLMs rapidly advance, increasing concerns arise regarding risks about actual authorship of texts we see online and in real world. The task of distinguishing LLM-authored texts is complicated by the nuanced and overlap…
Binary ClassificationLLM-generated Text Detectiontext-classificationText Classification+1Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT
The increasing prevalence of large language models (LLMs) has significantly advanced text generation, but the human-like quality of LLM outputs presents major challenges in reliably distinguishing between human-authored …
BenchmarkingLLM-generated Text DetectionQuestion AnsweringText Detection+1Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore
The efficacy of an large language model (LLM) generated text detector depends substantially on the availability of sizable training data. White-box zero-shot detectors, which require no such data, are nonetheless limited…
Language ModelingLanguage ModellingLarge Language ModelLLM-generated Text Detection+1Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection
Large language models (LLMs) have reached human-like proficiency in generating diverse textual content, underscoring the necessity for effective fake text detection to avoid potential risks such as fake news in social me…
LLM-generated Text DetectionText DetectionExploring Naive Approaches to Tell Apart LLMs Productions from Human-written Text
Powerful Large Language Models (large LMs or LLMs) such as BERT and GPT are making the task of detecting machine-generated text more and more prominent and crucial to minimize threats posed by text generation models misu…
LLM-generated Text DetectionSentenceText GenerationHow You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection
To combat the misuse of Large Language Models (LLMs), many recent studies have presented LLM-generated-text detectors with promising performance. When users instruct LLMs to generate texts, the instruction can include di…
Instruction FollowingLarge Language ModelLLM-generated Text DetectionMisinformation+2A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accep…
Binary ClassificationLLM-generated Text DetectionText DetectionOUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples
Large Language Models (LLMs) have achieved human-level fluency in text generation, making it difficult to distinguish between human-written and LLM-generated texts. This poses a growing risk of misuse of LLMs and demands…
Adversarial AttackAdversarial Attack DetectionDeepFake DetectionIn-Context Learning+4Red Teaming Language Model Detectors with Language Models
The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have pro…
Adversarial RobustnessLanguage ModelingLanguage ModellingLLM-generated Text Detection+3